Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks

Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge comput...

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Main Authors: Yupin Huang, Liping Qian, Anqi Feng, Ningning Yu, Yuan Wu
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8819986/
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spelling doaj-25d3ac474e064a2b8c81f0fe71c453782021-03-29T23:18:44ZengIEEEIEEE Access2169-35362019-01-01712398112399110.1109/ACCESS.2019.29382368819986Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing NetworksYupin Huang0Liping Qian1https://orcid.org/0000-0001-6210-2617Anqi Feng2Ningning Yu3Yuan Wu4College of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou, ChinaDepartment of Computer and Information Science, University of Macau, Macau, ChinaReal-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.https://ieeexplore.ieee.org/document/8819986/Short-term traffic predictiondeep belief networkhidden Markov modeledge computing
collection DOAJ
language English
format Article
sources DOAJ
author Yupin Huang
Liping Qian
Anqi Feng
Ningning Yu
Yuan Wu
spellingShingle Yupin Huang
Liping Qian
Anqi Feng
Ningning Yu
Yuan Wu
Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
IEEE Access
Short-term traffic prediction
deep belief network
hidden Markov model
edge computing
author_facet Yupin Huang
Liping Qian
Anqi Feng
Ningning Yu
Yuan Wu
author_sort Yupin Huang
title Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
title_short Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
title_full Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
title_fullStr Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
title_full_unstemmed Short-Term Traffic Prediction by Two-Level Data Driven Model in 5G-Enabled Edge Computing Networks
title_sort short-term traffic prediction by two-level data driven model in 5g-enabled edge computing networks
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Real-time and accurate short-term traffic prediction can effectively improve traffic efficiency, reduce accidents, and facilitate relevant departments to take reasonable traffic guidance measures. Therefore, we propose a two-level data driven model for short-term traffic prediction in an edge computing environment. Firstly, a Deep Belief Network (DBN) is developed to extract the traffic characteristics between the road occupancy and road flow collected by the deployed detectors. Then, we predict the developed future road flow of each road segment based on the output of the DBN, which would be used as one of the inputs of a Hidden Markov Model (HMM). Finally, a HMM is developed to predict the future road speed of each road segment characterizing the statistical relationship between the road flow and road speed. To validate the effectiveness of our proposed model, the data from the Performance Measurement System (PeMS) of the California Department of Transportation is applied. Simulation results show that our proposed model has better prediction performance in short-term traffic prediction than other models.
topic Short-term traffic prediction
deep belief network
hidden Markov model
edge computing
url https://ieeexplore.ieee.org/document/8819986/
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AT lipingqian shorttermtrafficpredictionbytwoleveldatadrivenmodelin5genablededgecomputingnetworks
AT anqifeng shorttermtrafficpredictionbytwoleveldatadrivenmodelin5genablededgecomputingnetworks
AT ningningyu shorttermtrafficpredictionbytwoleveldatadrivenmodelin5genablededgecomputingnetworks
AT yuanwu shorttermtrafficpredictionbytwoleveldatadrivenmodelin5genablededgecomputingnetworks
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